Adversarial Prompt Distillation for Vision-Language Models

• URL: http://arxiv.org/abs/2411.15244v2
• Date: Tue, 15 Apr 2025 01:57:20 GMT
• Title: Adversarial Prompt Distillation for Vision-Language Models
• Authors: Lin Luo, Xin Wang, Bojia Zi, Shihao Zhao, Xingjun Ma, Yu-Gang Jiang,
• Abstract summary: Adversarial Prompt Tuning (APT) applies adversarial training during the process of prompt tuning.<n>APD is a bimodal knowledge distillation framework that enhances APT by integrating it with multi-modal knowledge transfer.<n>Extensive experiments on multiple benchmark datasets demonstrate the superiority of our APD method over the current state-of-the-art APT methods.
• Score: 63.24270920122456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pre-trained Vision-Language Models (VLMs) such as Contrastive Language-Image Pre-training (CLIP) have been shown to be susceptible to adversarial attacks, raising concerns about their deployment in safety-critical applications like autonomous driving and medical diagnosis. One promising approach for robustifying pre-trained VLMs is Adversarial Prompt Tuning (APT), which applies adversarial training during the process of prompt tuning. However, existing APT methods are mostly single-modal methods that design prompt(s) for only the visual or textual modality, limiting their effectiveness in either robustness or clean accuracy. In this work, we propose Adversarial Prompt Distillation (APD), a bimodal knowledge distillation framework that enhances APT by integrating it with multi-modal knowledge transfer. APD optimizes prompts for both visual and textual modalities while distilling knowledge from a clean pre-trained teacher CLIP model. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our APD method over the current state-of-the-art APT methods in terms of both adversarial robustness and clean accuracy. The effectiveness of APD also validates the possibility of using a non-robust teacher to improve the generalization and robustness of fine-tuned VLMs.

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